Effect of the Fit2Thrive Intervention on Patient-reported Outcomes in Breast Cancer Survivors: A Randomized Full Factorial Trial
Bibliographic record
Abstract
BACKGROUND: Moderate to vigorous physical activity (MVPA) interventions improve patient-reported outcomes (PROs) of physical and psychological health among breast cancer survivors (BCS); however, the effects of specific intervention components on PROs are unknown. PURPOSE: To use the Multiphase Optimization Strategy (MOST) to examine overall effects of the Fit2Thrive MVPA promotion intervention on PROs in BCS and explore whether there are intervention component-specific effects on PROs. METHODS: Physically inactive BCS [n = 269; Mage = 52.5 (SD = 9.9)] received a core intervention (Fitbit + Fit2Thrive smartphone app) and were randomly assigned to one of 32 conditions in a full factorial experiment of five components ("on" vs. "off"): (i) support calls, (ii) deluxe app, (iii) text messages, (iv) online gym, and (v) buddy. Patient-Reported Outcomes Measurement Information System (PROMIS) questionnaires assessed anxiety, depression, fatigue, physical functioning, sleep disturbance and sleep-related impairment at baseline, post-intervention (12-week), and 24-week follow-up. Main effects for all components at each time point were examined using an intention to treat mixed-effects model. RESULTS: All PROMIS measures except sleep disturbance significantly improved (p's < .008 for all) from baseline to 12-weeks. Effects were maintained at 24-weeks. The "on" level of each component did not result in significantly greater improvements on any PROMIS measure compared to the "off" level. CONCLUSIONS: Participation in Fit2Thrive was associated with improved PROs in BCS, but improvements did not differ for "on" vs. "off" levels for any component tested. The low-resource Fit2Thrive core intervention is a potential strategy to improve PROs among BCS. Future studies should test the core in an RCT and examine various intervention component effects in BCS with clinically elevated PROs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".